Exploring Constraint Handling Techniques in Real-world Problems on MOEA/D with Limited Budget of Evaluations
This work addresses the challenge of effectively handling constraints in multi-objective optimization for researchers and practitioners using MOEA/D, offering an incremental improvement.
This paper explores the impact of different Constraint Handling Techniques (CHTs) on the MOEA/D solver for constrained Multi-objective Optimization Problems (MOPs). The authors introduce a new CHT called Three Stage Penalty, which achieved competitive results and remarkable hypervolume performance in a simulated car design MOP.
Finding good solutions for Multi-objective Optimization (MOPs) Problems is considered a hard problem, especially when considering MOPs with constraints. Thus, most of the works in the context of MOPs do not explore in-depth how different constraints affect the performance of MOP solvers. Here, we focus on exploring the effects of different Constraint Handling Techniques (CHTs) on MOEA/D, a commonly used MOP solver when solving complex real-world MOPs. Moreover, we introduce a simple and effective CHT focusing on the exploration of the decision space, the Three Stage Penalty. We explore each of these CHTs in MOEA/D on two simulated MOPs and six analytic MOPs (eight in total). The results of this work indicate that while the best CHT is problem-dependent, our new proposed Three Stage Penalty achieves competitive results and remarkable performance in terms of hypervolume values in the hard simulated car design MOP.